1. Field of the Invention
The present invention relates to an object recognition system suitable for grasping motions of an object or more in particular to an object recognition system suitable for tracking a moving vehicle or the like, on the one hand, and to a system for detecting an abnormal phenomenon on a road or the like, or more in particular to a system for processing and detecting an image on a TV camera.
2. Description of the Related Art
To recognize the movement or the like motion of an object by use of image processing is considered to provide a very effective means in various applications. For example, vehicles running on the road are recognized to measure the information such as the number and speed of vehicles passed, whether they are stationary or not, etc.
Conventional systems for recognizing vehicles by processing an image on the TV camera are described in "Vehicle Recognition by DTT Method", Computer Vision, Information Processing Society of Japan, 72-5, May 1992.
To detect the traffic condition is very effective in maintaining a smooth road traffic. Systems including what is called a loop-type vehicle sensor and an ultrasonic vehicle sensor have been used for detecting the traffic condition. These systems exert ultrasonic wave or magnetism at a ground point on the road, measure the existence of a vehicle according to the change thereof, and detect the number and speed of vehicles on the basis of the time of change. These systems, however, are basically capable of determining the traffic condition only at a single ground point and therefore are disadvantageous in measuring a wide range of conditions. For this reason, a method has positively been used recently, in which an image obtained from the TV camera is processed to measure the traffic condition, as described in JP-A-2-122400. According to the conventional system disclosed in JP-A-3-204783, on the other hand, a moving object is traced by center-of-gravity calculation of a binary-coded input shade image from the TV camera. Another conventional system disclosed in JP-A-62-180488 concerns character recognition but not the recognition of a mobile object. According to the last-mentioned method, a multi-valued template is prepared and decomposed into a plurality of binary templates, so that similarity between the binary template and a binary-coded input image is determined by pattern matching thereby to achieve character recognition.
The prior art relating to pattern matching is disclosed in JP-A-63-98070, etc.
Further, early detection of an abnormal phenomenon on the road is important in maintaining a smooth road traffic.
Specifically, it is necessary to detect an accident, a stationary vehicle, a fallen object or the like at an early time and prevent the secondary damage from being caused by such an abnormal phenomenon. Detection of an abnormal phenomenon in a tunnel is especially important. Systems applicable to such a purpose are expected to be developed more and more.
According to the conventional image processing systems, however, only what is called "the traffic flow data" including the number and speed of vehicles is measured, but the configuration thereof lacks means to detect various abnormal phenomena. An example of such a conventional traffic flow measuring system is disclosed in "Architecture of Traffic Flow Measuring System Using Image Processing" in a paper for Lecture at the 37th National Convention of Information Processing Society of Japan, 6T-6, 1988.
In the "Vehicle Recognition Using DTT Method" described above, an input image is differentiated and binary-coded, a binary projection distribution along X axis (horizontal direction) of this binary image is determined, and only the coordinates of this projection distribution beyond a predetermined threshold value are stored, thus determining the trace of vehicles. This process has been conventionally employed in most cases of measuring the number and speed of vehicles by image processing, thereby posing the problem that it is difficult to set a binary-coded threshold value on the one hand and measurement is difficult when vehicles are superposed one on another on the other.
According to the conventional techniques disclosed in JP-A-2-122400, JP-A-3-204783, JP-A-62-180488 and JP-A-63-98070, the number and speed of vehicles are measured by image processing in most cases through the processes of differentiation of input image, binary-coding and feature measurement. The problem of these methods is that a binary-coded threshold value cannot be easily set and measurement is difficult for vehicles superposed. Also, the conventional technique for binary-coding and center-of-gravity calculation of an input shade image encounters the problem that the image contrast is reduced by the change in the environment or situation in which the system is installed, thereby making it sometimes impossible to discriminate a vehicle from the background. The decomposition of a multi-valued template into a plurality of binary templates for pattern matching fails to recognize a moving object accurately.
As for abnormal phenomena in a tunnel, TV cameras are not actually installed at sufficiently short intervals to monitor the entire area in the tunnel. No one can predict where an abnormal phenomenon occurs. According to the conventional traffic flow measuring functions, therefore, it is virtually impossible for the conventional traffic flow measuring functions alone to measure abnormal phenomena occurring outside of the visual field of TV cameras. Another disadvantage of the conventional systems is that all abnormal phenomena cannot be grasped with the data on traffic flow.